Regression - Save Variable(s) - Probabilities of Each Response

Creates a new variable containing predicted probabilities of each response from a model (e.g., from Regression - Ordered Logitordered logit).

Output

Running this QScript will produce a new variablevariable, which will appear in your data setdata set for use in further analyses.

Technical details

Uses the probabilities method from R. Unlike the defaults in most R packages, this implementation assigns predicted values for observations not in the estimation sample.

Code

var __webpack_modules__ = ({});
// The module cache
var __webpack_module_cache__ = {};

// The require function
function __webpack_require__(moduleId) {

// Check if module is in cache
var cachedModule = __webpack_module_cache__[moduleId];
if (cachedModule !== undefined) {
return cachedModule.exports;
}
// Create a new module (and put it into the cache)
var module = (__webpack_module_cache__[moduleId] = {
exports: {}
});
// Execute the module function
__webpack_modules__[moduleId](module, module.exports, __webpack_require__);

// Return the exports of the module
return module.exports;

}

// webpack/runtime/rspack_version
(() => {
__webpack_require__.rv = () => ("1.7.2")
})();
// webpack/runtime/rspack_unique_id
(() => {
__webpack_require__.ruid = "bundler=rspack@1.7.2";
})();
includeWeb('QScript R Output Functions');

let selected_item = getSelectedROutputFromPage([]);
let item_non_null = selected_item !== null;
let r_classes = !!selected_item.outputClasses && selected_item.outputClasses;
let expected_class = selected_item !== null &&
    r_classes.indexOf('Regression') !== -1 &&
    r_classes.indexOf('BinaryLogitRegression') === -1 &&
    r_classes.indexOf('OrderedLogitRegression') === -1 &&
    r_classes.indexOf('MultinomialLogitRegression') === -1 &&
    r_classes.indexOf('PoissonRegression') === -1 &&
    ['Binary Logit', 'Ordered Logit', 'Multinomial Logit', 'Poisson'].indexOf(selected_item.data.get('type')) != -1 ?
    'Regression' : ['BinaryLogitRegression', 'OrderedLogitRegression', 'MultinomialLogitRegression', 'PoissonRegression'];
saveVariables('Probabilities', 'Regression', 'flipData::Probabilities(', ')', null, null, 'prob', expected_class);